World Neurosurg
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Risk assessment is critically important in elective and high-risk interventions, particularly spine surgery. This narrative review describes the evolution of risk assessment from the earliest instruments focused on general surgical risk stratification, to more accurate and spine-specific risk calculators that quantified risk, to the current era of big data. ⋯ RATs have undergone a transformative shift from generalized risk stratification to quantitative predictive models. The next generation of tools will likely involve integration of radiographic and genetic biomarkers, machine learning, and artificial intelligence to improve the accuracy of these models and better inform patients, surgeons, and payers.
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Review
Neurosurgery and social media: A bibliometric analysis of scientific production from 2004-2023.
Neurosurgery is a rapidly advancing surgical specialty. Social media has significantly impacted the landscape of advancements in the field of neurosurgery. Research on the subject of neurosurgery and social media plays a vital role in combating disability and mortality due to neurological diseases, especially in trauma-affected individuals by increasing cooperation and sharing of clinical experiences between neurosurgeons via social media. This study aimed to evaluate the global neurosurgery and social media research performance from 2004 to 2023. ⋯ Exploring neurosurgery on social media enhances global collaboration, utilizing dynamic platforms for real-time knowledge exchange and holds immense potential for the field's global advancement.
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Spinal cord injury (SCI) is a significant public health issue, leading to physical, psychological, and social complications. Machine learning (ML) algorithms have shown potential in diagnosing and predicting the functional and neurologic outcomes of subjects with SCI. ML algorithms can predict scores for SCI classification systems and accurately predict outcomes by analyzing large amounts of data. This systematic review aimed to examine the performance of ML algorithms for diagnosing and predicting the outcomes of subjects with SCI. ⋯ ML and deep learning (DL) have shown great potential in various aspects of SCI. ML and DL algorithms have been employed multiple times in predicting and diagnosing patients with SCI. While there are studies on diagnosing acute SCI using DL algorithms, further research is required in this area.
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The balance between comprehensive intraoperative neurophysiological monitoring (IONM) for both upper and lower limbs while ensuring the reliability of motor evoked potentials (MEPs) is paramount in motor area surgery. It is commonly difficult to obtain good simultaneous stimulation of both upper and lower limbs. A series of factors can bias MEP accuracy, and inappropriate stimulation intensity can result in unreliable monitoring. The presented IONM technique is based on the concurrent use of both transcranial and cortical strip electrodes to facilitate simultaneous monitoring of both upper and lower limbs at optimized stimulation intensities to increase IONM accuracy during motor area surgery. ⋯ The technique was demonstrated to be accurate in providing a good simultaneous neurophysiological evaluation of both upper and lower limbs with an optimized and stimulation amplitude. The technique results in a low encumbrance of electrodes in the surgical field. Our results have confirmed the "proof of concept," its reliability and feasibility.
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To evaluate variability in aneurysm detection and the potential of artificial intelligence (AI) software as a screening tool by comparing conventional computed tomography angiography (CTA) images (standard care) with AI software. ⋯ AI has the potential to increase the diagnosis of unruptured intracranial aneurysms. However, it must be used as an adjacent tool within the standard of care due to limited applicability in real-world settings.